1. Field of the Invention
The present invention relates generally to the field of epileptic event detection. More particularly, it concerns epileptic event detection by use of an autoregression algorithm on a time series of patient body signal data.
2. Description of Related Art
There have been various advancements in the area of seizure detection, which remains a fairly subjective endeavor. The task of automated detection of epileptic seizures is generally related to and dependent on the definition of what is a seizure, definition which to date is subjective and thus inconsistent within and among experts. The lack of an objective and universal definition complicates not only the task of validation and comparison of detection algorithms, but also (and possibly more importantly), the characterization of the spatio-temporal behavior of seizures and of other dynamical features required to formulate a comprehensive epilepsy theory.
The current state of automated seizure detection is, by extension, a reflection of the power and limitations of visual analysis, upon which it rests. The subjectivity intrinsic to expert visual analysis of seizures and its incompleteness (it cannot adequately quantify or estimate certain signal features, such as power spectrum) confound the objectivity and reproducibility of results of signal processing tools used for automated seizure detection. What is more, several of the factors that enter into the determination of whether or not certain grapho-elements should be classified as a seizure are non-explicit (“gestalt-based”) and thus difficult to articulate, formalize and program into algorithms.
Most, if not all, existing seizure detection algorithms are structured to operate as expert electroencephalographers. Thus, seizure detection algorithms that apply expert-based rules are at once useful and deficient; useful as they are based on a certain fund of irreplaceable clinical knowledge, and deficient as human analysis biases propagate into their architecture. These cognitive biases which pervade human decision processes and which have been the subject of formal inquiry are rooted in common practice behaviors such as: a) The tendency to rely too heavily on one feature when making decisions (e.g., if onset is not sudden, the event is unlikely to be characterized as a seizure because seizures are paroxysmal events); b) To declare objects as equal if they have the same external properties (e.g., this is a seizure because it is just as rhythmical as those we score as seizures) or c) relying on the ease with which associations come to mind (e.g., this pattern looks just like the seizures we reviewed yesterday).
Seizure detection algorithms' mixed results make attainment of a unitary or universal seizure definition ostensibly difficult. In addition to cognitive biases, the inadequacy of many seizure detection algorithms may also be attributable in part, to the distinctiveness in the architecture and parameters of each algorithm. The fractal or multi-fractal structures of seizures accounts at least in part for the differences in results, and draws attention to the so-called “Richardson effect.” Richardson demonstrated that the length of borders between countries (a natural fractal) is a function of the size of the measurement tool, increasing without limit as the tool's size is reduced. Mandelbrot, in his seminal contribution “How long is the coast of Britain,” stressed the complexities inherent to the Richardson effect, due to the dependency of particular measurements on the scale of the tool used to perform them. Although defining seizures as a function of a detection tool would be acceptable, this approach may be impracticable when comparisons between, for example, clinical trials or algorithms are warranted. Another strategy to bring unification of definitions is to universally adopt the use of one method, but this would be to the detriment of knowledge mining from seizure-time series and by extension to clinical epileptology.
To date, meaningful performance comparisons among myriad existing algorithms have not been feasible due to lack of a common and adequate database. However, even if adequate databases were available, the value of such “comparisons” would be limited by the absence of a universally accepted definition of what is a “seizure.” The previously noted cognitive biases and architectural/parametric distinctions among algorithms impede achievement of consensus and in certain cases even of majority agreement in classifying particular events as seizures or non-seizures. Because expert visual analysis provides the benchmarks (seizure onset and termination times) from which key metrics (detection latency in reference to electrographic and clinical onset time (“speed of detection”), sensitivity, specificity and positive predictive value) are derived, the effects of cognitive biases propagate beyond the seizure/non-seizure question into other aspects of the effectiveness of a particular seizure detection algorithm.